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David Beier

Publications and source records attributed to David Beier.

2 recordsLinked to original sources

Autoimmunity as the consequence of a spontaneous mutation in Rasgrp1.

A mouse strain was identified with a recessive genetic lesion, which spontaneously developed a lymphoproliferative autoimmune syndrome exhibiting features of systemic lupus erythematosus. Positional mapping of the disease-associated locus revealed a lesion in Rasgrp1 that prevented the translation of the RasGRP1 protein. T cells from these mice failed to activate Ras or proliferate vigorously following antigen encounter and showed defects in positive selection. Peripheral RasGRP1lag T cells spontaneously adopted a memory phenotype and were able to transfer disease to lymphopenic recipient mice. CD4+ T cells accumulated in the lymphoid tissues of older RasGRP1lag mice and were resistant to activation-induced cell death. RasGRP1lag B cells were functionally normal, but activated B cells were detected in older mice, as were autoantibodies directed against self-antigens. Our findings indicate that Ras signaling pathways are required to maintain T cell tolerance and to prevent autoimmune disease.

Animals↗

Models for microarray gene expression data.

This paper describes a general methodology for the analysis of differential gene expression based on microarray data. First, we characterize the data by a linear statistical model that accounts for relevant sources of variation in the data and then we consider estimation of the model parameters. Because microarray studies typically involve thousands of genes, we propose a two-stage method for parameter estimation. The interaction terms for genes and experimental conditions in this model capture all relevant information about differential gene expression in the microarray data. We propose a mixture distribution model for a summary statistic of differential expression that consists of null and alternative component distributions. The mixture model suggests two methods for identifying genes exhibiting differential expression. One is a frequentist method that identifies distinguished genes and the other an empirical Bayes procedure that yields estimated posterior probabilities of differential expression, conditional on observed microarray readings.

Animals↗